A 10-m annual grazing intensity dataset in 2015–2021 for the largest temperate meadow steppe in China

Mapping grazing intensity (GI) using satellites is crucial for developing adaptive utilization strategies according to grassland conditions. Here we developed a monitoring framework based on a paired sampling strategy and the classification probability of random forest algorithm to produce annual grazing probability (GP) and GI maps at 10-m spatial resolution from 2015 to 2021 for the largest temperate meadow in China (Hulun Buir grasslands), by harmonized Landsat 7/8 and Sentinel-2 images. The GP maps used values of 0–1 to present detailed grazing gradient information. To match widely used grazing gradients, annual GI maps with ungrazed, moderately grazed, and heavily grazed levels were generated from the GP dataset with a decision tree. The GI maps for 2015–2021 had an overall accuracy of more than 0.97 having significant correlations with the statistical data at city (r = 0.51) and county (r = 0.75) scales. They also effectively captured the GI gradients at site scale (r = 0.94). Our study proposed a monitoring approach and presented annual 10-m grazing information maps for sustainable grassland management.


Fig. S1 .
Fig. S1.Number of observations.(a) the number of total observations of harmonized Landsat 7/8 and Sentinel-2 in 2015-2021.(b) the number of good observations of harmonized Landsat 7/8 and Sentinel-2 in 2015-2021.(c) the number of total observations of only Landsat 7/8.(d) the number of good observations of only Landsat 7/8.

Fig. S2 .
Fig. S2.The fire events in Hulun Buir happened from 2015 to 2021 based on MODIS data.

Fig. S3 .
Fig. S3.Spatial and temporal dynamics of grazing probability from 2015 to 2021 at the county scale.(a) The mean grazing probability of every county from 2015 to 2021.The inset figure shows the mean annual grazing probability of Hulun Buir from 2015 to 2021.(b-n) The annual mean grazing probability and corresponding trend of each county were shown using blue dot lines and red dash lines in the figure.

Table S1 .
Accuracy assessment of grazing intensity map in 2021 for the ungrazed and heavily grazed types based on the validation region of interests (ROIs) from field survey and Google Earth images.The confusion matrix was calculated at the pixel scale.This table shows the User's (UA), Producer's (PA), and Overall (OA) accuracies of each grazing intensity type.The kappa coefficient in 2021 was 0.934.

Table S2 .
Accuracy assessment of grazing intensity map in 2020 for the ungrazed and heavily grazed types based on the validation region of interests (ROIs) from field survey and Google Earth images.The confusion matrix was calculated at the pixel scale.This table shows the User's (UA), Producer's (PA), and Overall (OA) accuracies of each grazing intensity type.The kappa coefficient in 2020 was 0.989.

Table S3 .
Accuracy assessment of grazing intensity map in 2019 for the ungrazed and heavily grazed types based on the validation region of interests (ROIs) from field survey and Google Earth images.The confusion matrix was calculated at the pixel scale.This table shows the User's (UA), Producer's (PA), and Overall (OA) accuracies of each grazing intensity type.The kappa coefficient in 2019 was 0.990.

Table S4 .
Accuracy assessment of grazing intensity map in 2018 for the ungrazed and heavily grazed types based on the validation region of interests (ROIs) from field survey and Google Earth images.The confusion matrix was calculated at the pixel scale.This table shows the User's (UA), Producer's (PA), and Overall (OA) accuracies of each grazing intensity type.The kappa coefficient in 2018 was 0.991.

Table S5 .
Accuracy assessment of grazing intensity map in 2017 for the ungrazed and heavily grazed types based on the validation region of interests (ROIs) from field survey and Google Earth images.The confusion matrix was calculated at the pixel scale.This table shows the User's (UA), Producer's (PA), and Overall (OA) accuracies of each grazing intensity type.The kappa coefficient in 2017 was 0.992.

Table S6 .
Accuracy assessment of grazing intensity map in 2016 for the ungrazed and heavily grazed types based on the validation region of interests (ROIs) from field survey and Google Earth images.The confusion matrix was calculated at the pixel scale.This table shows the User's (UA), Producer's (PA), and Overall (OA) accuracies of each grazing intensity type.The kappa coefficient in 2016 was 0.977.

Table S7 .
Accuracy assessment of grazing intensity map in 2015 for the ungrazed and heavily grazed types based on the validation region of interests (ROIs) from field survey and Google Earth images.The confusion matrix was calculated at the pixel scale.This table shows the User's (UA), Producer's (PA), and Overall (OA) accuracies of each grazing intensity type.The kappa coefficient in 2015 was 0.985.